Multi-Robot Systems From Swarms to Intelligent Automata - Parker et al (Eds) Part 3 docx

Sensor Network-Mediated Multi-Robot Task Allocation 35 bars, and hence is favored for use in this application since it provides reduced bounds on system run time over a simple Raster Scan method We also compared mobility requirements for DINTA and RS methods Specifically, the use of mobility requires energy A measure of energy for mobility is determined for the purposes of comparison by computing the total time of the robot motion Figure shows a comparison of energy consumption in units of time-in-motion As expected, DINTA outperforms Raster Scan significantly However as the number of events increases to infinity, DINTA will approach Raster Scan energy consumption Also note, that on the interval [5,20] the slope of the Raster Scan curve is very small and the energy consumption is insensitive to event arrival rate Field Trials using NIMS The third, and final, set of experiments discussed here were performed in field trials with the NIMS system We used our task allocation system and compared two policies - Time (tasks with smaller time stamp get priority) and Distance (tasks closer to the robot get priority) A set of experiments was conducted on a NIMS setup deployed in the James San Jacinto Mountain Reserve Because of space limitations, only representative graphs are presented Figure shows the representative PAR data from sensor collected during the operation of the Time policy (Figure 5a) and the Distance policy (Figure 5b) Figure also shows points in time when events were generated and serviced by both policies for sensor Note that events are generated in response to fluctuations in PAR As shown on Figure 5, events are generated proportionally to the density of the ‘spikes’ in PAR data and cover all significant ‘spikes’ of PAR data Figure 5c shows the comparison between the cumulative event OnTime of the Time policy and the Distance policy For visualization purposes, in Figure 5c event’s OnTime is presented as a zero-mean Gaussian distribution It follows that the Distance policy has smaller average OnTime with smaller deviation Summary We presented a novel, sensor network-mediated, approach to multi robot task allocation Our algorithm DINTA: Distributed In-Network Task Allocation solves the online multi robot task allocation problem This approach allows us to combine the benefits of a sensor network with the mobility and functionality of robots The system computes task assignments distributively in-network while, at the same time, providing a virtual sensor and communication device that ‘extends’ throughout the whole environment There are several advantages in using DINTA as opposed to traditional MRTA approaches The 36 Batalin and Sukhatme 350 800 sensor event generated event serviced 300 700 sensor event generated event serviced 600 250 500 PAR PAR 200 400 150 300 100 200 50 100 9:37 10:01 10:42 11:03 Time of day Time of day (a) Time policy (b) Distance policy −5 1.4 x 10 p = Time p = Distance 1.2 0.8 0.6 0.4 0.2 −1.5 −1 −0.5 0.5 1.5 x 10 (c) Event OnTime Figure NIMS field experiments for two policies a,b) PAR data acquired by the first sensor during one of the field experiments Events generated and serviced are shown for Time and Distance policies Note that events are rendered time of occurrence vs the PAR value of the event c) Event OnTime in a form of a zero-mean Gaussian distributions for Time and Distance policies The OnTime of events generated by all sensors is considered Dotted (blue or lighter) graphs show the distributions at original means Sensor Network-Mediated Multi-Robot Task Allocation 37 sensor network allows a robot to detect a goal (alarm, event) even though the alarm is not in the robot’s sensor range In addition, robots can use the sensor network to relay messages if they are not within communication range of each other Further, robots can be very simple and potentially heterogeneous We also presented physical experimental results of using DINTA for field measurements in natural setting using a monitoring infrastructure composed of mobile robots on cables and network nodes in the vicinity of the cable transect Acknowledgments This work is supported in part by the National Science Foundation (NSF) under grants IIS-0133947, EIA-0121141 and grants CCR-0120778, ANI-00331481 (via subcontract) Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and not necessarily reflect the views of the National Science Foundation Notes For implementation details of DINTA see ((Batalin and Sukhatme, 2004b).) For experimental and other details see ((Batalin et al., 2004a)) References Batalin, M., Rahimi, M., Yu, Y., Liu, S., Kansal, A., Sukhatme, G., Kaiser, W., Hansen, M., Pottie, G., Srivastava, M., and Estrin, D (2004a) Call and Response: Experiments in Sampling the Environment In Proc ACM SenSys Batalin, M and Sukhatme, G (2004a) Coverage, Exploration and Deployment by a Mobile Robot and Communication Network Telecommunication Systems Journal, Special Issue on Wireless Sensor Networks, 26(2):181–196 Batalin, M and Sukhatme, G (2004b) Using a Sensor Network for Distributed Multi-Robot Task Allocation In Proc IEEE International Conference on Robotics and Automation, pages 158–164, New Orleans, Louisiana Batalin, M., Sukhatme, G., and Hattig, M (2004b) Mobile Robot Navigation using a Sensor Network In Proc IEEE International Conference on Robotics and Automation, pages 636– 642, New Orleans, Louisiana Botelho, S and Alami, R (2000) M+: A Scheme for Multi-Robot Cooperation through Negotiated Task Allocation and Achievement In Proc IEEE International Conference on Robotics and Automation (ICRA), pages 293–298 Gerkey, B., Vaughan, R., and Howard, A (2003) The Player/Stage Project: Tools for MultiRobot and Distributed Sensor Systems In Proc International Conference on Advanced Robotics (ICAR 2003), pages 317–323, Coimbra, Portugal Gerkey, B P and Matarict’, M J (2004) A Formal Analysis and Taxonomy of Task Allocation in Multi-Robot Systems Intl Journal of Robotics Research, 23(9):939–954 Koenig, S and Simmons, R G (1992) Complexity Analysis of Real-Time Reinforcement Learning Applied to Finding Shortest Paths in Deterministic Domains Technical Report CMU-CS-93-106, Carnegie Mellon University, School of Computer Science, Carnegie Mellon University, Pittsburg, PA 15213 38 Batalin and Sukhatme NIMS (2004) http://www.cens.ucla.edu/portal/nims.html Parker, L E (1998) ALLIANCE: An Architecture for Fault-Tolerant Multi-Robot Cooperation In IEEE Transactions on Robotics and Automation, volume 14, pages 220–240 Pister, K S J., Kahn, J M., and Boser, B E (1999) Smart Dust: Wireless Networks of Millimeter-Scale Sensor Nodes Electronics Research Laboratory Research Summary Werger, B B and Matarit’c, M J (2000) Distributed Autonomous Robotic Systems 4, chapter Broadcast of Local Eligibility for Multi-Target Observation, pages 347–356 SpringerVerlag II COORDINATION IN DYNAMIC ENVIRONMENTS MULTI-OBJECTIVE COOPERATIVE CONTROL OF DYNAMICAL SYSTEMS Zhihua Qu Department of Electrical and Computer Engineering, University of Central Florida, Orlando, FL 32816, USA ∗ qu@mail.ucf.edu Jing Wang Department of Electrical and Computer Engineering, University of Central Florida, Orlando, FL 32816, USA jwang@pegasus.cc.ucf.edu Richard A Hull Lockheed Martin Missiles and Fire Control, 5600 Sand Lake Road, Orlando, FL 32819, USA Richard.A.Hull@lmco.com Abstract In this paper, the cooperative control problem of making system’s different outputs converge to different steady states is studied for a general class of MIMO dynamic systems with finite but arbitrary relative degree A set of less-restrictive conditions on the design of cooperative control feedback matrices are established In particular, the proposed design does not require either that collaborative robots have a fixed communication/control structure (such as leader/followr or nearest neighbor) or that their sensor/communication graph be irreducible Keywords: Cooperative control, dynamical systems, multi-objective Introduction Recently, the cooperative control problem of making the states of a group of dynamical systems converge to same steady state has stirred a great deal of interests since its solvability implies the solvability of general formation stabilization problem which has direct applications in many fields requiring ∗ The research is supported in part by a grant from Lockheed Martin Corporation 41 L.E Parker et al (eds.), Multi-Robot Systems From Swarms to Intelligent Automata Volume III, 41–52 c 2005 Springer Printed in the Netherlands 42 Qu, et al multiple inexpensive mobile systems, such as deploying a group of vehicles for search or exploration purpose in hazardous environments Motivated by the flocking behavior of birds in flight, Reynolds introduced a computer animation model for cohesion, separation and alignment in (Reynolds, 1987) Subsequently, a simple discrete-time model (Vicsek model) was given in (Vicsek et al., 1995) for the heading alignment of autonomous particles moving in the plane Simulation results verified the correctness of Vicsek model More recently, a theoretical explanation of Vicsek model was presented in (Jadbabaie et al., 2003) by using graph theory The conditions on the connectivity of undirected sensor graphs are given for the overall system convergence This result was extended to networks with directed sensor graphs in (Lin et al., 2004)(Moreau, 2003) Other recent relevant works include the consensus problems in (Saber and Murray, 2003)(Ren and Beard, 2004) Most of the aforementioned works only considered simple linear systems with single or double integrator models Moreover, for multiple output system, it is desirable in practice that the system’s different outputs converge to different steady states In our recent works (Qu et al., 2004a)(Qu et al., 2004b), we extend the results in (Jadbabaie et al., 2003)(Lin et al., 2004) to a general class of MIMO dynamical systems of finite but arbitrary relative degree In particular, a leaderfollower cooperative control strategy was analyzed in (Qu et al., 2004a) and general leaderless cooperative control was studied in (Qu et al., 2004b) In this paper, we continue the works in (Qu et al., 2004a)(Qu et al., 2004b), and study the problem of making systems’ different channels (outputs) as well as the corresponding states converge to different steady states The proposed design does not require either that collaborative robots have a fixed communication/control structure (such as leader/followr or nearest neighbor) or that their sensor/communication graph be irreducible The convergence of the overall system is rigorously proved by studying the convergence of products of a sequence of row stochastic matrices An illustrative example is provided to verify the proposed method Preliminaries Let p be the p-dimensional column vector with all its elements being 1, and Jr1 ×r2 ∈ ℜr1 ×r2 be a matrix whose elements are all Im is the m−dimensional identity matrix ⊗ denotes the Kronecker product A nonnegative matrix has all entries nonnegative A square real matrix is row stochastic if it is nonnegative and its row sums all equal For a row stochastic matrix E, define δ(E) = max j maxi1 ,i2 |Ei1 j − Ei2 j |, which measures how different the rows of E are Also, define λ(E) = − mini1 ,i2 ∑ j min(Ei1 j , Ei2 j ) Given a sequence of nonnegative matrix E(k), the notation of E(k) 0, k = 0, 1, · · · , means Multi-Objective Cooperative Control of Dynamical Systems 43 that, there is a sub-sequence {lv , v = 1, · · · , ∞} of {0, 1, 2, · · · , ∞} such that limv→∞ lv = +∞ and E(lv ) = A non-negative matrix E is said to be reducible if the set of its indices, I = {1, 2, · · · , n}, can be divided into two disjoint nonempty sets S = {i1 , i2 , · · · , iµ } and S c = I/S = { j1 , j2 , · · · , jν } (with µ+ ν = n) such that Eiα jβ = 0, where α = 1, · · · , µ and β = 1, · · · , ν Matrix E is said to be irreducible if it is not reducible A square matrix E ∈ ℜn×n can be used to define a directed graph with n nodes v1 , · · · , , and there is a directed arc from vi to v j if and only if Ei j = A directed graph represented by E is strongly connected if between every pair of distinct nodes vi , v j there is a directed path of finite length that begins at vi and ends at v j The fact that a directed graph represented by E is strongly connected is equivalent to that matrix E is irreducible (Minc, 1988) The following lemma provides a necessary and sufficient condition on the irreducibility of a non-negative matrix in a special structure, which has direct relation to the canonical system model discussed in this paper Lemma 2.1 (Qu et al., 2004a) Given any non-negative matrix E ∈ ℜ(qm)×(qm) with sub-blocks Ei j ∈ ℜm×m , let E = [E i j ] ∈ ℜ(Lm)×(Lm) with L = l1 + · · · + lq be defined by E ii = I(li −1) ⊗ Im Eii , Ei j = 0 Ei j where li ≥ are positive integers for i = 1, · · · , q Then, E is irreducible if and only if E is irreducible The classical convergence result of the infinite products of sequences of row stochastic matrices (Wolfowitz, 1963) has been applied in the study of coordination behavior of groups of mobile autonomous agents (Jadbabaie et al., 2003)(Lin et al., 2004) In our recent works (Qu et al., 2004a)(Qu et al., 2004b), we relaxed the condition in (Wolfowitz, 1963) and found an easy-tocheck condition on the convergence of a sequence of row stochastic matrices in the lower-triangular structure, and also extended it to the case of the products of lower-triangular matrices and general matrices These new results are useful for establishing less-restrictive conditions on the design of cooperative control and the connectivity requirements among individual systems In what follows, we recall these two results without proof for briefness Lemma 2.2 (Qu et al., 2004a) Consider a sequence of nonnegative, row stochastic matrices P(k) ∈ ℜR×R in the lower-triangular structure, where R = ∑m ri , sub-blocks Pii (k) on the diagonal are square and of dimension ℜri ×ri , i=1 sub-blocks Pi j (k) off diagonal are of appropriate dimensions Suppose that Pii (k) ≥ εi Jri ×ri for some constant εi > and for all (i = 1, · · · , m), and in the ith row of P(k) (i > 1), there is at least one j ( j < i) such that Pi j Then, 44 Qu, et al limk→∞ ∏k−1 P(k − l) = 1R c, where constant vector c = [c1 , 0, · · · , 0] ∈ ℜ1×R l=0 with c1 ∈ ℜ1×r1 Lemma 2.3 (Qu et al., 2004b) Given sequences of row stochastic matrices P(k) ∈ ℜR×R and P (k) ∈ ℜR×R , where P(k) is in the lower-triangular structure and P (k) satisfying Pii (k) ≥ ε p > Then, k−1 lim ∏ P(k − l)P (k − l) = 1R c1 , k→∞ l=0 if and only if limk→∞ ∏k−1 P(k − l) = 1R c2 , where c1 and c2 are constant vecl=0 tors Problem Formulation Consider a group of MIMO dynamical systems given by ˙ xi = Ai xi + Bi ui , yi = Ci xi , ηi = gi (ηi , xi ), ˙ where i = 1, · · · , q, li ≥ is an integer, xi order Jordan canonical form given by ⎡ −1 ··· ⎢ ⎢ −1 ⎢ ⎢ Jk = ⎢ ⎢ 0 · · · −1 ⎢ ⎣ 0 ··· 0 ··· (1) ∈ ℜli m , ηi ∈ ℜni −li m , Jk is the kth 0 ⎤ ⎥ ⎥ ⎥ ⎥ ⎥ ∈ ℜk×k , ⎥ ⎥ −1 ⎦ −1 ∈ ℜ(li m)×m , Ci = Im ∈ ℜm×(li m) , Im ˙ ui is the cooperative control law to be designed, and subsystem ηi = gi (ηi , xi ) is input-to-state stable Without loss of any generality, in this paper we consider the case that l1 = l2 = · · · = lq = l Ai = Jli ⊗ Im ∈ ℜ(li m)×(li m) , Bi = Remark 3.1 Model (1) defines a general class of MIMO dynamical systems with finite but arbitrary relative degree The system models considered in most of recent works (Jadbabaie et al., 2003)(Lin et al., 2004)(Saber and Murray, 2003) are either single or double integrator, which can be transformed into the given canonical model (1) with relative degree or 2, respectively In gen˙ eral, for a robot whose dynamics are given by φi = fi (φi , vi ), yi = hi (φi ), it is possible to find a diffeomorphic state transformation xi = Ti (φi ) and a decentralized control law vi (φi , ui ) such that its dynamics are transformed into (1) It is straightforward to verify that an input-output feedback linearizable Multi-Objective Cooperative Control of Dynamical Systems 45 system with stable internal dynamics can be transformed into (1) For nonholonomic mobile robot, it is also possible to transform its model into (1) by using dynamic feedback linearization technique under some mild conditions The problem of making the systems’ outputs and the states of individual systems converge to the same steady state has been studied, such as in (Jadbabaie et al., 2003)(Lin et al., 2004) for single integrator models and in (Qu et al., 2004a)(Qu et al., 2004b) for general dynamical systems like (1) In this paper, the objective is to synthesize a general class of cooperative controls and find the less-restrictive conditions on the connectivity requirements such that systems’ different channels (outputs) as well as the corresponding states converge to different steady states For ease of reference, we call this as multi-objective convergence Multi-Objective Cooperative Control In general, we assume that the system sensors have a limited field of view, such as cone like of view, in that the system can only get the information from other systems in a relative direction and distance of itself The control ui for the ith system will be determined according to the output feedback information of itself as well as that of its near neighbors The connectivity topology among individual systems can be represented by a signal transmission matrix, S(t) = [Si j (t)] ∈ ℜq×q , where Sii = which means that system always has the information from itself; Si j = if no signal is received by the ith system from the jth system, otherwise Si j = If the directed sensor graph is strongly connected, that is, the signal transmission matrix S(t) is irreducible, the convergence result has been obtained in (Lin et al., 2004) for linear system with single integrator model, and it is further extended to a general class of systems (1) (Qu et al., 2004a) However, this connectivity requirement is still too strong for cooperative control of a group of systems considering the limitations of sensor resources and communication burdens This motivates us to study whether the overall system will still converge even when the directed sensor graphs are always not strongly connected (that is, the signal transmission matrices are reducible) When the control objective is to make all the states of individual systems converge to the same steady state, the question has been addressed in (Qu et al., 2004b) and a less-restrictive condition on the irreducibility of signal transmission matrix was given In this section, we will study the multi-objective convergence problem and further extend the work in (Qu et al., 2004b)(Qu et al., 2004a) by establishing less-restrictive conditions on connectivity requirements and proposing a criterion for the design of cooperative feedback control matrices 46 4.1 Qu, et al The Proposed Cooperative Control Let the cooperative control be given by the following linear equation: for i = 1, · · · , q, ui = ∑ j∈Ni (t) N Gi j (t)y j = Gi (t)y, (2) where Ni (t) denotes the set of labels of robot i’s neighbor robots at time t, Gi j ∈ ℜm×m is the feedback gain matrix, Gi j (t) ≥ and Gi j = 0, Gi = Gi1 · · · Giq with Gi j ∈ ℜm×m is the interconnection matrix satisfying the properties that [y1 Gi 1mq = 1m , and y = [ T · · · yT ]T It is easy to see from (2) that Gi j = Gi j if q j ∈ Ni (t), otherwise Gi j = Assume that G(t) will change over time according to physical surroundings, and Gi (t) be piecewise constant for all i G Let {tk : k = 0, 1, · · · } with t0 = t0 be the sequence of time instants at which tG tG tG G(t) changes That is, G(t) = G(tk ) over the time interval t ∈ [tk ,tk+1 ) If tG there are only finite changes for G(t), that is, for t > tiG , G(t) = G(tiG ), we can always partition the remaining time to generate an infinite time interval G G [tk ,tk+1 ) Suppose that < tmin ≤ tk+1 − tk ≤ tmax It follows from (1) and (2) tG tG that x = (A + D(t))x, ˙ (3) T T [xT T where x = [ , · · · , xq ]T ∈ ℜNq , Nq = mql, xi = [ i1 , xi2 , · · · , xil ]T ∈ ℜml , xi j = [xT [ [xi j1 , xi j2 , · · · , xi jm ]T ∈ ℜm with i = 1, · · · , q, and j = 1, · · · , l, A = diag{A1 , · · · , N N Aq } ∈ ℜNq ×Nq , C = diag{C1 , · · · , Cq } ∈ ℜ(mq)×Nq , B = diag{B1 , · · · , Bq } ∈ T ) ℜNq ×(mq) ,G = GT · · · GT ∈ ℜ(mq)×(mq , and D = BGC q It follows from the special structures of matrices A and D(t) that A + D(t) = ¯ ¯ ¯ −INq + D(t), where D(t) = [Di j (t)] with I ¯ Dii = I(l−1) ⊗ Im Gii ¯ ∈ ℜlm×lm , Di j = 0 Gi j ∈ ℜlm×lm , where i, j = 1, · · · , q, i = j To achieve the multi-objective convergence, we need the following assumption: Assumption 4.1 Assume that the feedback matrix G(t) satisfying the condition that sub-blocks Gi j (t) = diag{Gi j,ss (t)}, s = 1, · · · , m for all i and j Let us rearrange the states of overall system and lump the different output and its corresponding relative states together by defining a state transformation z = T x as follows: z = [zT , · · · , zT ]T , where zi = [zT , zT , · · · , zT ]T ∈ ℜql , zi j = m iq i1 i2 Multi-Objective Cooperative Control of Dynamical Systems 47 [zi j1 , · · · , zi jl j ]T ∈ ℜl , i = 1, · · · , m, j = 1, · · · , q, and zi1 : zi11 = x11i , zi j : zi j1 = x j1i , ziq : ziq1 = xq1i zi12 = x12i zi j2 = x j2i , ziq2 = xq2i , ··· ··· zi jl = x jli , ziql = xqli zi1l = x1li (4) It is easy to see that T is a permutation matrix, which permutes the rows of x to obtain z It follows from (3) and (4) that ˜ z = −(INq − D(t))z, ˙ I (5) ¯ ˜ ˜ ˜ where D = T DT −1 = diag{D11 , · · · , Dmm } with (s = 1, · · · , m) ⎤ ⎡ 0 ··· 0 Il −1 ⎢ G11,ss G12,ss · · · G1q,ss ⎥ ⎥ ⎢ ⎢ 0 ⎥ 0 Il −1 · · · ⎥ ⎢ ⎥ ⎢ ˜ Dss = ⎢ G21,ss G22,ss · · · G2q,ss ⎥ ∈ ℜql×ql ⎢ ⎥ ⎥ ⎢ ⎥ ⎢ ⎣ 0 0 ··· Il −1 ⎦ Gq1,ss Gq2,ss · · · Gqq,ss 4.2 Conditions on Multi-Objective Convergence In this subsection, we establish a set of less-restrictive conditions on feedback matrix G(t) such that the multi-objective convergence is achieved when the signal transmission matrices are reducible It is shown in (Minc, 1988) that, if S(t) is reducible, then there is a permutation matrix T1 ∈ ℜq×q such that T ST1 (t) = T1T S(t)T1 is in the lower-triangular structure, that is ⎤ ⎡ ··· ST1 ,11 (t) ⎥ ⎢ ST ,21 (t) ST ,22 (t) · · · ⎥ ⎢ ST1 (t) = ⎢ ⎥, ⎦ ⎣ ST1 ,k1 (t) ST1 ,k2 (t) · · · ST1 ,kk (t) where ST1 ,ii ∈ ℜqi ×qi , ∑k qi = q, and ST1 ,ii (t) are irreducible Correspondingly, i=1 we have augmented permutation matrices T2 = T1 ⊗ Im ∈ ℜqm×qm and T3 = T diag{T1 ⊗ Il , · · · , T1 ⊗ Il } ∈ ℜqlm×qlm , such that GT2 (t) = T2T G(t)T2 is also in T the lower-triangular structure, and the state transformation is z = T3 ξ To this end, the system dynamics (5) become ˙ ˜T ξ = −(INq − T3T DT3 )ξ I (6) 48 Qu, et al Remark 4.1 Generally, the case of qi = means that the q systems reformulate k subgroups with k < q Example 4.1 Consider the case of robots with m = and l = We have [xT T T T [x Nq = For states x = [ , x2 , x3 , x4 ]T with xi = [ i1 , xi2 ]T , i = 1, · · · , 4, it is easy to find transformation T such that z = T x = [zT , zT ]T with ˜ ¯ [x [x z1 = [ 11 , x21 , x31 , x41 ]T and z1 = [ 12 , x22 , x32 , x42 ]T Thus, we have D = T DT −1 = ˜ ˜ ˜ 11 , D22 }, with Dss = [Gi j,ss ], s = 1, 2, i, j = 1, · · · , Let the reducible diag{D signal transmission matrix S and permutation matrix T1 be ⎡ ⎡ ⎤ ⎤ 1 1 0 ⎢ 0 ⎥ ⎢ ⎥ ⎥ , T1 = ⎢ 0 ⎥ S=⎢ ⎣ 1 ⎦ ⎣ 0 ⎦ 0 1 0 ST1 ,11 , with ST1 ,21 ST1 ,22 ⎡ ⎤ ⎡ ⎤ 1 ST1 ,11 = 1, ST1 ,21 = ⎣ ⎦ , ST1 ,22 = ⎣ 1 ⎦ , 0 1 Then we have ST1 = where ST1 ,11 and ST1 ,22 are irreducible To this end, four robots is reformulated into two subgroups, that is, group = {2} and group = {1, 3, 4} We have the GT2 ,11 , with GT2 ,11 = corresponding T2 = T1 ⊗ I2 such that GT2 = GT2 ,21 GT2 ,22 G22 and ⎡ ⎡ ⎤ ⎤ G12 G11 G14 GT2 ,21 = ⎣ ⎦ , GT2 ,22 = ⎣ G31 G33 ⎦ 0 G43 G44 ˜T ˜ Also, T3 = diag{T1 , T1 }, and DT3 = T3T DT3 = T ˜ T1T D11 T1 TD T ˜ 22 T1 , where ˜ T DsT3 ,11 ˜ T , with DsT3 ,11 = G22,ss and ˜ T ˜ T DsT3 ,21 DsT3 ,22 ⎤ ⎡ ⎡ ⎤ G12,ss G11,ss G14,ss ˜ T ˜ T ⎦ DsT3 ,21 = ⎣ ⎦ , DsT3 ,22 = ⎣ G31,ss G33,ss 0 G43,ss G44,ss ˜ T1T Dss T1 = In what follows, we present the main result of the paper and give a lessrestrictive condition on the design of cooperative control feedback matrix Multi-Objective Cooperative Control of Dynamical Systems 49 Assumption 4.2 Assume that the signal transmission matrix S(tk ) is reducible tG tG for almost all k, and GT2 (tk ) is in the lower-triangular form Define a subsequence {sv , v = 0, 1, · · · , ∞} of {0, 1, 2, · · · , ∞}, s0 = and limv→∞ sv = +∞, G tG such that for all time instants tsv permutation matrices T2 (tsv ) take the same value Assume that GT2 (tsv ) has the following properties: (i) for the nonzero tG element in the i1 th row i2 th column of irreducible matrix ST1 ,ii (tsv ), the corretG tG sponding i1 th row i2 th column sub-blocks of matrix GT2 ,ii (tsv ) which is m × m dimensional has all its diagonal elements positive; (ii) for every i > 1, there is tG at least one j such that GT2 ,i j (tsv ) 0, j < i Theorem 4.1 Consider the cooperative control of system (1) using (2) under assumptions 4.1 and 4.2 Then, lim x(t) = 1ql ⊗ cx(0), t→∞ (7) N c ∈ ℜm×Nq is a constant matrix Proof: For briefness, we denote f (tk ) = f (k) for symbol f The proof of (7) tG is equivalent to that of lim z(t) = diag{1ql c1 , · · · , 1ql cm }z(0), t→∞ (8) N where c1 , · · · , cm ∈ ℜ1×ql are row vectors and c = diag{c1 , c2 , · · · , cm } ∈ ℜm×Nq It follows that the solution of (6) is given by k tG ξ(tk+1 ) = ∏ P(k − l)ξ(t0 ), tG (9) l=0 T where P(i) = e−(I−T3 (i)D(ti z(k) = T3 (k)ξ(k) that T ˜ G )T (i))(t G −t G ) T3 i+1 i , i = 0, · · · , k It follows from (9) and k T tG z(tk+1 ) = ∏ T3 (k − l)P(k − l)T3 (k − l)T z(t0 ), tG (10) l=0 ˜ T Since T3 (k) = diag{T1 , · · · , T1 } and D(k) are in the diagonal structure, we have G ˜ −(I−T1T (i)Dss (i)T1 (i))(ti+1 −tiG ) T T Pss (i) = e , and k tG P T tG zs (tk+1 ) = ∏ T1 (k − l)Pss (k − l)T1 (k − l)T zs (t0 ), (11) l=0 where s = 1, · · · , m Thus, we only need to show that limt→∞ zs (t) = 1ql cs zs (0) It suffices to prove that k P T lim ∏ T1 (k − l)Pss (k − l)T1 (k − l)T = 1ql cs k→∞ l=0 (12) 50 Qu, et al It follows from GT2 (tk ) is in the lower-triangular structure that tG ˜ tG T ˜ T DsT3 = T1T (k)Dss (tk )T1 (k) and Pss (k) are also in the lower-triangular structure Moreover, Pss (k) is row-stochastic matrix and its diagonal elements are lowerbounded by a positive value (Freedman, 1983) By assumption 4.2 that the i1 th tG row i2 th column sub-blocks GT2 ,ii (tsv ) has all its diagonal elements positive and ˜ T tG ST1 ,ii (tsv ) is irreducible, we have that DsT3 ,ii (tsv ) is irreducible by lemma 2.1 tG and Pss,ii (sv ) > (Qu et al., 2004a) On the other hand, GT2 ,i j (tsv ) leads tG ˜ T tG to DsT3 ,i j (tsv ) and Pss,i j (sv ) It then follows from assumption 4.2 and lemma 2.2 that P lim Pss (sv )Pss (sv−1 ) · · · Pss (s0 ) = 1ql cs , v→∞ (13) P T where cs is a constant vector Define Pss (sv ) = T1 (sv )T T1 (sv −1)Pss (sv −1)T1T (sv − P T T 1) · · · T1 (sv−1 + 1)Pss (sv−1 + 1)T1T (sv−1 + 1)T1 (sv−1 ) It then follows from (13) and the fact that Pss (sv ) has positive diagonal elements that, (12) can be proved using lemma 2.3 This completes the proof An Illustrative Example Consider a group of three nonholonomic 4-wheel differential driven mobile robots By taking the robot “hand” position as the guide point, whose model can be feedback linearized to a double integrator with a stable internal dynamics (Qu et al., 2004a): ˙ zi1 = zi2 , zi2 = vi2 , ˙ where i = 1, 2, 3, zi1 = [zi11 , zi12 ]T ∈ ℜ2 is the position of particle i, zi2 = [zi21 , zi22 ]T ∈ ℜ2 its velocity, and vi = [vi1 , vi2 ]T ∈ ℜ2 its acceleration inputs The cooperative control objective is that all particles move to the same position but with different horizontal and vertical coordinates value Define the state and input transformations xi1 = zi1 , xi2 = xi1 + zi2 , vi = −2xi2 + xi1 + ui Then ˙ system model can be transformed into xi1 = −xi1 + xi2 , xi2 = −xi2 + ui ,where ˙ u = Gy with y = [ 11 , x21 , x31 ]T To this end, the cooperative control method in [xT T T this paper can be used for the design of G For illustration purpose, assume that two kinds of leader-follower situations appear during the robot motion process: (i) robot as the leader, robot follows robot and robot follows robot 2; (ii) robot as the leader, robot follows robot and robot follows robot After permutation T1 and T2 , the corresponding cooperative feedback matrices take the form ⎤ ⎤ ⎡ ⎡ G11 G22 0 GT1 = ⎣ G21 G22 ⎦ , GT2 = ⎣ G12 G11 ⎦ G32 G33 G31 G33 51 Multi-Objective Cooperative Control of Dynamical Systems To satisfy the condition in theorem 4.1, let GT1 and GT2 be chosen as ⎡ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎣ ⎤ 0 0 0 0 0 0.2 0.8 0 0 0.2 0.8 0 0.2 0 0.8 0 0.2 0 0.8 ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎦ The initial positions are [6, 3]T , [2, 5]T and [4, 2]T , respectively Figure shows the convergence of robots’ position, which verifies the proposed design in this paper robot robot robot 1 Figure 6 Convergence of positions under cooperative control Conclusion In this paper, the multi-objective convergence problem has been studied for a general MIMO dynamical systems The obtained less-restrictive conditions on the design of cooperative feedback matrices require neither the strong connectivity of system sensor graphs nor the fixed communication structure for robots The proposed method can be applied to the formation stabilization and formation tracking control of robots References Freedman, D (1983) Markov Chains Springer-Verlag, New York Jadbabaie, A., Lin, J., and Morse, A S (2003) Coordination of groups of mobile autonomous agents using nearest neighbor rules IEEE Trans on Automatic Control, 48:988–1001 Lin, Z., Brouchke, M., and Francis, B (2004) Local control strategies for groups of mobile autonomous agents IEEE Trans on Automatic Control, 49:622–629 Minc, H (1988) Nonnegative Matrices John Wiley & Sons, New York, NY 52 Qu, et al Moreau, L (2003) Leaderless coordination via bidirectional and unidirectional time-dependent communication In Proceedings of the 42nd IEEE Conference on Decision and Control, Maui, Hawaii Qu, Z., Wang, J., and Hull, R A (2004a) Cooperative control of dynamical systems with application to mobile robot formation In The 10th IFAC/IFORS/IMACSIFIP Symposium on Large Scale Systems: Theory and Applications, Japan Qu, Z., Wang, J., and Hull, R A (2004b) Products of row stochastic matrices and their applications to cooperative control for autonomous mobile robots In Technique report; also submitted to 2005 American Control Conference Ren, W and Beard, R W (2004) Consensus of information under dynamically changing interaction topologies In Proceedings of the American Control Conference, Boston Reynolds, C W (1987) Flocks, herds, and schools: a distributed behavioral model Computer Graphics (ACM SIGGRAPH 87 Conference Proceedings), 21(4):25–34 Saber, R O and Murray, R M (2003) Consensus protocols for networks of dynamic agents In Proceedings of the American Control Conference, Denver, CO Vicsek, T., Czirok, A., Jacob, E B., Cohen, I., and Shochet, O (1995) Novel type of phase transition in a system of self-driven particles Physical Review Letters, 75:1226–1229 Wolfowitz, J (1963) Products of indecoposable, aperiodic, stochastic matrices Proc Amer Mathematical Soc., 14:733–737 LEVELS OF MULTI-ROBOT COORDINATION FOR DYNAMIC ENVIRONMENTS Colin P McMillen, Paul E Rybski, Manuela M Veloso School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213, U.S.A mcmillen@cs.cmu.edu, prybski@cs.cmu.edu, veloso@cs.cmu.edu Abstract RoboCup, the international robot soccer competition, poses a set of extremely difficult challenges for multi-robot systems To be competitive in RoboCup’s rapidly-changing, dynamic, adversarial environment, teams need to make use of effective coordination strategies We describe some of our experiences with effective coordination of robots teams and introduce several levels of strategies which encapsulate coordination from the level of individual robots to synchronized coordination of the entire team Keywords: Adversarial environments, robot soccer, multi-robot coordination Introduction The RoboCup robot soccer competition is a domain in which teams must address the challenges of real-time perception, cognition, and action Robots must be able to operate in a very dynamic environment in which they must reason not only about the actions of their teammates, but also about the actions of a team of adversarial agents Teams of robots that operates without an effective teamwork strategy are likely to hamper each other’s efforts and perform as an inferior team Our primary research interests are in exploring the scientific challenges of developing effective teamwork strategies for autonomous robotic systems where all sensing and cognition is done on-board the robot We have had extensive experience with robot positioning ranging from early work in simulated robot soccer players (Veloso et al., 1999) to the Sony AIBO league (Uther et al., 2002) In this previous work, we made use of artificial potential field methods and have found them to be a very powerful way of representing multiple constraints when positioning robots However, there are a number of limitations in the kinds of behaviors that potential fields can express We are actively exploring other coordination strategies which we will describe in more detail 53 L.E Parker et al (eds.), Multi-Robot Systems From Swarms to Intelligent Automata Volume III, 53–64 c 2005 Springer Printed in the Netherlands 54 McMillen, et al This paper focuses on developing team strategies for robots that compete in the RoboCup (Kitano et al., 1997) legged league We discuss various approaches for teamwork and cooperation, and describe some empirical results from experiments 1.1 Related Work Potential field methods have been used very successfully for navigation tasks such as obstacle avoidance (Khatib, 1985) This idea has been extended such that a group of robots can maintain formations while using only local information in their potential calculations (Balch and Arkin, 1998, Balch and Hybinette, 2000) Several groups have encoded domain-specific heuristics into potential fields In the RoboCup domain, potential fields can be constructed that guide robots to an area near the opponent’s goal or to an open position that is well-suited for pass reception (Castelpietra et al., 2001, Veloso et al., 1999, Weigel et al., 2001) A behavior architecture that relies on potential fields for motion planning and action selection is described in (Laue and Röfer, 2004) Their approach has been applied to real robots, including the German Team of the RoboCup legged league Potential field methods have several limitations that have been reported in the literature, including susceptibility to local minima and oscillations (Koren and Borenstein, 1991) An approach known as forward chaining dynamically reshapes the potential field using heuristics that guide the robot to the goal through a series of subgoals or waypoints that attempt to avoid local minima (Bell and Weir, 2004) The forward chaining approach is particularly interesting to us because it utilizes the idea of a positioning function that changes over time 1.2 Coordination in Robot Soccer We have been researching coordination strategies for the different RoboCup robot soccer leagues for several years RoboCup robot soccer in general (Kitano et al., 1997) offers a very challenging research environment for multirobot systems One of the most interesting aspects of the RoboCup leagues is that we change the rules of the game and the playing environment every year in order to increase the difficulty of the problem towards matching real setups as much as possible Of particular interest to this paper is our work in the RoboCup legged league with the Sony AIBO four-legged robots, which need to be fully autonomous with on-board sensing, computation, and action The legged league in particular has gone through several changes since 1998 Some of the most significant Levels of Multi-Robot Coordination for Dynamic Environments 55 changes occurred in 2002 and have made the most impact in our multi-robot research efforts Communicating robots: The AIBO robots are now equipped with the ability to communicate wirelessly In the initial years, when the robots could not communicate, we achieved teamwork through vision of the position of the ball – the robots’ behaviors were vision-servoed Three attacker robots searched for the ball; as soon as they saw the ball, they would move towards it and then move the ball towards the opponent goal Because the ball was often not in the robots’ field of view, due to the occlusion by other robots and the robots’ own search for localization markers, not all the robots could see the ball Teamwork was a property that emerged due to this discontinuity of ball perception: when one robot saw the ball, it would move toward the ball, tending to block the ball from the view of its teammates Because of this occlusion, the teammates would remain spread out from the “attacker.” Now that communication is available between robots, we have researched methods of sharing information (Roth et al., 2003) and dynamically changing robot roles and positioning (Vail and Veloso, 2003) In this paper, we discuss the different levels of dynamic coordination necessary in the presence of skilled opponent teams We present the solutions that we have developed and plan to continue researching World space increase: The field’s size has increased by approximately 50% from its initial size, and the number of robots in a team has increased from three to four The increase in field size makes it infeasible for robots to see across the entire field In the initial smaller field, individual robots could recognize objects across the complete field Modeling the world state is now a task that needs to combine a robot’s own perception and the communicated information from other robots Multiple robots need to build an accurate world model and select joint actions that fit a team policy Rules of the game: The rules of the game set constraints on the legal positioning and actions of robots For example, only one robot is allowed to defend the goal area This type of rule creates hard constraints on the dynamic positioning of team members In addition, robots encounter difficult motion situations when surrounded by opponent robots In these situations, a team member may need the help of robot teammates This is a challenge that requires an effective dynamic coordination algorithm that monitors the progress of teammates In addition, teamwork should change as a function of the opponent team, the specific state of the field, and the remaining time of the game  ... the distributions at original means Sensor Network-Mediated Multi-Robot Task Allocation 37 sensor network allows a robot to detect a goal (alarm, event) even though the alarm is not in the robot’s... (Qu et al. , 2004a)(Qu et al. , 2004b), we extend the results in (Jadbabaie et al. , 20 03) (Lin et al. , 2004) to a general class of MIMO dynamical systems of finite but arbitrary relative degree In particular,... potential fields can express We are actively exploring other coordination strategies which we will describe in more detail 53 L.E Parker et al (eds.), Multi-Robot Systems From Swarms to Intelligent Automata